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Experimental investigation of the fluidization behavior in single and binary solidliquid fluidized beds of nonspherical particles as solid phase and water as liquid phase was performed in a Perspex column. Different particle sizes were used to prepare single and binary mixtures with different weight ratios for fluidization. Minimum fluidization velocity increased with increasing average particle size and decreasing sphericity for the binary mixture. An empirical correlation was developed to predict the minimum fluidization velocity. Genetic algorithm-artificial neural network (GA-ANN) modeling was applied to predict the minimum fluidization velocity for single and binary solid-liquid fluidized beds. The application of GA-ANN analysis leads to designing binary solid-liquid fluidization systems without experimentation.

Expansion behaviour for a bed of binary mixture of the irregularly shaped particle in Newtonian liquid was measured in two different circular columns. Variations in the physical parameters on the expansion behaviour have been reported. Bed expansion increases with an increase in liquid velocity and a decrease in particle diameter. Static bed height and expansion of the bed are low for higher diameter columns. An empirical correlation has been developed for predicting the ratio of bed height at the fluidized condition to the initial bed height as a function of the physical and dynamic variables related to the system for the binary particle mixtures. The correlation coefficient and variance of the estimate are 0.9299 and 0.0013, respectively, which is acceptable statistical accuracy. A hybrid of the genetic algorithm and neural network modelling for the prediction of the same has also been attempted where the input parameters are optimized using the Levenberg–Marquardt algorithm. With a relative error of 1.46%, the genetic algorithm performed well. So, the modelling has successfully predicted the bed height ratio at fluidized conditions to the initial bed height.